lecture 32 - iowa state universityclass.ece.iastate.edu/ee435/lectures/ee 435 lect 32... ·...
TRANSCRIPT
![Page 1: Lecture 32 - Iowa State Universityclass.ece.iastate.edu/ee435/lectures/EE 435 Lect 32... · 2018-04-02 · Distortion Analysis T T S mN 1 0 m h - 1 N 2 A m P d d k 0 THEOREM: Consider](https://reader034.vdocument.in/reader034/viewer/2022050114/5f4b7a266fa6b235086da60a/html5/thumbnails/1.jpg)
EE 435
Lecture 32
Spectral Performance – Windowing
Spectral Performance of Data Converters- Time Quantization
- Amplitude Quantization
Quantization Noise
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Distortion AnalysisT
TS
1-hm01mNΧN
2A Pm
0kΧ
THEOREM: Consider a periodic signal with period T=1/f and sampling
period TS=1/fS. If NP is an integer and x(t) is band limited to fMAX, then
and for all k not defined above
where is the DFT of the sequence
N=number of samples, NP is the number of periods, and
1N
0kkΧ
1N
0kSkTx
MAX
P
f 1h = Int
f N
Key Theorem central to Spectral Analysis that is widely used !!! and often “abused”
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Considerations for Spectral
Characterization
•Tool Validation (MATLAB)
•FFT Length
•Importance of Satisfying Hypothesis
•Windowing
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Spectral Response (expressed in dB)
Note Magnitude is Symmetric wrt fSAMPLEP
SIGNALAXISN
1nff
(Actually Stem plots but points connected
in plotting program)
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Input Waveform.•
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Example
)sin(.)sin( t250tVIN
WLOG assume fSIG=50Hz
Consider NP=20.2 N=4096
SIGπf2ω
Recall 20log10(0.5)=-6.0205999
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Input Waveform.•
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Spectral Response.•
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Example
)sin(.)sin( t250tVIN
WLOG assume fSIG=50Hz
Consider NP=20.01 N=4096
SIGπf2ω
Deviation from hypothesis is .05% of the sampling window
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Input Waveform
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Input Waveform
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Input Waveform
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Input Waveform
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Spectral Response with Non-Coherent Sampling
(zoomed in around fundamental)
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Columns 1 through 7
-89.8679 -83.0583 -77.7239 -74.2607 -71.6830 -69.5948 -67.8044
Columns 8 through 14
-66.2037 -64.7240 -63.3167 -61.9435 -60.5707 -59.1642 -57.6859
Columns 15 through 21
-56.0866 -54.2966 -52.2035 -49.6015 -46.0326 -40.0441 -0.0007
Columns 22 through 28
-40.0162 -46.2516 -50.0399 -52.8973 -55.3185 -57.5543 -59.7864
Columns 29 through 35
-62.2078 -65.1175 -69.1845 -76.9560 -81.1539 -69.6230 -64.0636
Fundamental will appear at position 1+Np = 21
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Columns 36 through 42
-59.9172 -56.1859 -52.3380 -47.7624 -40.9389 -6.0401 -39.2033
kth harmonic will appear at position 1+k•Np
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Observations
• Modest change in sampling window of
0.01 out of 20 periods (.05%) still results in
a modest error in both fundamental and
harmonic
• More importantly, substantial raise in the
computational noise floor !!! (from over -
300dB to only -40dB)
• Errors at about the 6-bit level !
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Example
)sin(.)sin( t250tVIN
WLOG assume fSIG=50Hz
Consider NP=20.001 N=4096
SIGπf2ω
Deviation from hypothesis is .005% of the sampling window
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Spectral Response with Non-coherent Sampling
(zoomed in around fundamental)
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Columns 1 through 7
-112.2531 -103.4507 -97.8283 -94.3021 -91.7015 -89.6024 -87.8059
Columns 8 through 14
-86.2014 -84.7190 -83.3097 -81.9349 -80.5605 -79.1526 -77.6726
Columns 15 through 21
-76.0714 -74.2787 -72.1818 -69.5735 -65.9919 -59.9650 0.0001
Columns 22 through 28
-60.0947 -66.2917 -70.0681 -72.9207 -75.3402 -77.5767 -79.8121
Columns 29 through 35
-82.2405 -85.1651 -89.2710 -97.2462 -101.0487 -89.5195 -83.9851
Fundamental will appear at position 1+Np = 21
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Columns 36 through 42
-79.8472 -76.1160 -72.2601 -67.6621 -60.7642 -6.0220 -59.3448
Columns 43 through 49
-64.8177 -67.8520 -69.9156 -71.4625 -72.6918 -73.7078 -74.5718
Columns 50 through 56
-75.3225 -75.9857 -76.5796 -77.1173 -77.6087 -78.0613 -78.4809
Columns 57 through 63
-78.8721 -79.2387 -79.5837 -79.9096 -80.2186 -80.5125 -80.7927
kth harmonic will appear at position 1+k•Np
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Observations
• Modest change in sampling window of
0.01 out of 20 periods (.005%) results in a
small error in both fundamental and
harmonic
• More importantly, substantial raise in the
computational noise floor !!! (from over -
300dB to only -60dB)
• Errors at about the 10-bit level !
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Spectral Response with Non-coherent sampling
(zoomed in around fundamental)
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Columns 1 through 7
-130.4427 -123.1634 -117.7467 -114.2649 -111.6804 -109.5888 -107.7965
Columns 8 through 14
-106.1944 -104.7137 -103.3055 -101.9314 -100.5575 -99.1499 -97.6702
Columns 15 through 21
-96.0691 -94.2764 -92.1793 -89.5706 -85.9878 -79.9571 0.0000
Columns 22 through 28
-80.1027 -86.2959 -90.0712 -92.9232 -95.3425 -97.5788 -99.8141
Columns 29 through 35
-102.2424 -105.1665 -109.2693 -117.2013 -120.8396 -109.4934 -103.9724
Fundamental will appear at position 1+Np = 21
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Columns 36 through 42
-99.8382 -96.1082 -92.2521 -87.6522 -80.7470 -6.0207 -79.3595
Columns 43 through 49
-84.8247 -87.8566 -89.9190 -91.4652 -92.6940 -93.7098 -94.5736
Columns 50 through 56
-95.3241 -95.9872 -96.5810 -97.1187 -97.6100 -98.0625 -98.4821
Columns 57 through 63
-98.8732 -99.2398 -99.5847 -99.9107 -100.2197 -100.5135 -100.7937
Columns 64 through 70
kth harmonic will appear at position 1+k•Np
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Observations
• Modest change in sampling window of
0.001 out of 20 periods (.0005%) results in
a small error in both fundamental and
harmonic
• More importantly, substantial raise in the
computational noise floor !!! (from over -
300dB to only -80dB)
• Errors at about the 13-bit level !
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Considerations for Spectral
Characterization
• Tool Validation
• FFT Length
• Importance of Satisfying Hypothesis- NP is an integer
- Band-limited excitation
• Windowing
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FFT Examples
Recall the theorem that provided for the relationship between the
DFT terms and the Fourier Series Coefficients required
1. The sampling window be an integral number of periods
2.P
SIGNAL
Nf
f2N max
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Example
If fSIG=50Hz
and NP=20 N=512
P
SIGNAL
Nf
f2N max fmax< 640Hz
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Example
)sin(.)sin(.)sin( t1450t250tVIN
If fSIG=50Hz
Consider NP=20 N=512
SIGπf2ω
Recall 20log10(0.5)=-6.0205999
(i.e. a component at 700 Hz which violates the
band limit requirement)
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Effects of High-Frequency Spectral Components
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Effects of High-Frequency Spectral Components
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Effects of High-Frequency Spectral Components
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Effects of High-Frequency Spectral Components
Columns 1 through 7
-296.9507 -311.9710 -302.4715 -302.1545 -310.8392 -304.5465 -293.9310
Columns 8 through 14
-299.0778 -292.3045 -297.0529 -301.4639 -297.3332 -309.6947 -308.2308
Columns 15 through 21
-297.3710 -316.5113 -293.5661 -294.4045 -293.6881 -292.6872 -0.0000
Columns 22 through 28
-301.6889 -288.4812 -292.5621 -292.5853 -294.1383 -296.4034 -289.5216
Columns 29 through 35
-285.9204 -292.1676 -289.0633 -292.1318 -290.6342 -293.2538 -296.8434
fhigh=14fo
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Effects of High-Frequency Spectral Components
Columns 36 through 42
-301.7087 -307.2119 -295.1726 -303.4403 -301.6427 -6.0206 -295.3018
Columns 43 through 49
-298.9215 -309.4829 -306.7363 -293.0808 -300.0882 -306.5530 -302.9962
Columns 50 through 56
-318.4706 -294.8956 -304.4663 -300.8919 -298.7732 -301.2474 -293.3188
fhigh=14fo
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Effects of High-Frequency Spectral Components
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Effects of High-Frequency Spectral Components
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Effects of High-Frequency Spectral Components
(zoomed in around fundamental)
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Effects of High-Frequency Spectral Components
ff2f samplealias
Columns 225 through 231
-296.8883 -292.8175 -295.8882 -286.7494 -300.3477 -284.4253 -282.7639
Columns 232 through 238
-273.9840 -6.0206 -274.2295 -284.4608 -283.5228 -297.6724 -291.7545
Columns 239 through 245
-299.1299 -305.8361 -295.1772 -295.1670 -300.2698 -293.6406 -304.2886
Columns 246 through 252
-302.0233 -306.6100 -297.7242 -305.4513 -300.4242 -298.1795 -299.0956
Aliased components at
233611201f
fN1sequenceinpositionthus
f611f14f8122f
sig
aliasp
sigsigsigalias
.
..
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Effects of High-Frequency Spectral Components
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Effects of High-Frequency Spectral Components
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Observations
• Aliasing will occur if the band-limited part of the hypothesis for using the DFT is not satisfied
• Modest aliasing will cause high frequency components that may or may not appear at a harmonic frequency
• More egregious aliasing can introduce components near or on top of fundamental and lower-order harmonics
• Important to avoid aliasing if the DFT is used for spectral characterization
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Considerations for Spectral
Characterization
• Tool Validation
• FFT Length
• Importance of Satisfying Hypothesis- NP is an integer
- Band-limited excitation
• Windowing
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Are there any strategies to address the
problem of requiring precisely an integral
number of periods to use the FFT?
Windowing is sometimes used
Windowing is sometimes misused
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WindowingWindowing is the weighting of the time
domain function to maintain continuity at
the end points of the sample window
Well-studied window functions:
• Rectangular (also with appended zeros)
• Triangular
• Hamming
• Hanning
• Blackman
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Rectangular Window
Sometimes termed a boxcar window
Uniform weight
Can append zeros
Without appending zeros equivalent to no window
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Rectangular Window
)sin(.)sin( t250tVIN
Assume fSIG=50Hz
Consider NP=20.1 N=512
SIGπf2ω
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Rectangular Window
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(zoomed in around fundamental)
Spectral Response with Non-coherent sampling
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Rectangular Window (with appended zeros)
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Rectangular Window
Columns 1 through 7
-48.8444 -48.7188 -48.3569 -47.7963 -47.0835 -46.2613 -45.3620
Columns 8 through 14
-44.4065 -43.4052 -42.3602 -41.2670 -40.1146 -38.8851 -37.5520
Columns 15 through 21
-36.0756 -34.3940 -32.4043 -29.9158 -26.5087 -20.9064 -0.1352
Columns 22 through 28
-19.3242 -25.9731 -29.8688 -32.7423 -35.1205 -37.2500 -39.2831
Columns 29 through 35
-41.3375 -43.5152 -45.8626 -48.0945 -48.8606 -46.9417 -43.7344
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Rectangular Window
Columns 1 through 7
-48.8444 -48.7188 -48.3569 -47.7963 -47.0835 -46.2613 -45.3620
Columns 8 through 14
-44.4065 -43.4052 -42.3602 -41.2670 -40.1146 -38.8851 -37.5520
Columns 15 through 21
-36.0756 -34.3940 -32.4043 -29.9158 -26.5087 -20.9064 -0.1352
Columns 22 through 28
-19.3242 -25.9731 -29.8688 -32.7423 -35.1205 -37.2500 -39.2831
Columns 29 through 35
-41.3375 -43.5152 -45.8626 -48.0945 -48.8606 -46.9417 -43.7344
Energy spread over several frequency components
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Triangular Window
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Triangular Window
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(zoomed in around fundamental)
Spectral Response with Non-Coherent Sampling and Windowing
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Triangular Window
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Triangular Window
Columns 1 through 7
-100.8530 -72.0528 -99.1401 -68.0110 -95.8741 -63.9944 -92.5170
Columns 8 through 14
-60.3216 -88.7000 -56.7717 -85.8679 -52.8256 -82.1689 -48.3134
Columns 15 through 21
-77.0594 -42.4247 -70.3128 -33.7318 -58.8762 -15.7333 -6.0918
Columns 22 through 28
-12.2463 -57.0917 -32.5077 -68.9492 -41.3993 -74.6234 -46.8037
Columns 29 through 35
-77.0686 -50.1054 -77.0980 -51.5317 -75.1218 -50.8522 -71.2410
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Hamming Window
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Hamming Window
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(zoomed in around fundamental)
Spectral Response with Non-Coherent Sampling and Windowing
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Comparison with Rectangular Window
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Hamming Window
Columns 1 through 7
-70.8278 -70.6955 -70.3703 -69.8555 -69.1502 -68.3632 -67.5133
Columns 8 through 14
-66.5945 -65.6321 -64.6276 -63.6635 -62.6204 -61.5590 -60.4199
Columns 15 through 21
-59.3204 -58.3582 -57.8735 -60.2994 -52.6273 -14.4702 -5.4343
Columns 22 through 28
-11.2659 -45.2190 -67.9926 -60.1662 -60.1710 -61.2796 -62.7277
Columns 29 through 35
-64.3642 -66.2048 -68.2460 -70.1835 -71.1529 -70.2800 -68.1145
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Hanning Window
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Hanning Window
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(zoomed in around fundamental)
Spectral Response with Non-Coherent Sampling and Windowing
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Comparison with Rectangular Window
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Hanning Window
Columns 1 through 7
-107.3123 -106.7939 -105.3421 -101.9488 -98.3043 -96.6522 -93.0343
Columns 8 through 14
-92.4519 -90.4372 -87.7977 -84.9554 -81.8956 -79.3520 -75.8944
Columns 15 through 21
-72.0479 -67.4602 -61.7543 -54.2042 -42.9597 -13.4511 -6.0601
Columns 22 through 28
-10.8267 -40.4480 -53.3906 -61.8561 -68.3601 -73.9966 -79.0757
Columns 29 through 35
-84.4318 -92.7280 -99.4046 -89.0799 -83.4211 -78.5955 -73.9788
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Comparison of 4 windows
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Comparison of 4 windows
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Preliminary Observations about Windows
• Provide separation of spectral components
• Energy can be accumulated around
spectral components
• Simple to apply
• Some windows work much better than
others
But – windows do not provide dramatic
improvement and …
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Comparison of 4 windows when sampling
hypothesis are satisfied
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Comparison of 4 windows
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Preliminary Observations about Windows
• Provide separation of spectral components
• Energy can be accumulated around
spectral components
• Simple to apply
• Some windows work much better than
others
But – windows do not provide dramatic
improvement and can significantly degrade
performance if sampling hypothesis are met
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Issues of Concern for Spectral Analysis
An integral number of periods is critical for spectral analysis
Not easy to satisfy this requirement in the laboratory
Windowing can help but can hurt as well
Out of band energy can be reflected back into bands of interest
Characterization of CAD tool environment is essential
Spectral Characterization of high-resolution data converters
requires particularly critical consideration to avoid simulations or
measurements from masking real performance
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• Distortion Analysis
• Time Quantization Effects
– of DACs
– of ADCs
• Amplitude Quantization Effects
– of DACs
– of ADCs
Spectral Characterization of Data
Converters
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Will leave the issues of time-quantization and
DAC characterization to the student
These concepts are investigated in the following
slides
Concepts are important but time limitations
preclude spending more time on these topics in
this course
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• Distortion Analysis
• Time Quantization Effects
– of DACs
– of ADCs
• Amplitude Quantization Effects
– of DACs
– of ADCs
Spectral Characterization of Data
Converters
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Quantization Effects on Spectral
Performance and Noise Floor in DFT
Matlab File: afft_Quantization.m
• Assume the effective clock rate (for either an ADC or a DAC) is arbitrarily
fast
• Without Loss of Generality it will be assumed that fSIG=50Hz
• Index on DFT will be listed in terms of frequency (rather than index number)
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Quantization Effects
16,384 pts res = 4bits NP=2520 msec
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Quantization Effects
16,384 pts res = 4bits NP=2520 msec
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Quantization Effects
16,384 pts res = 4bits
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Quantization Effects
Simulation environment:
NP=23
fSIG=50Hz
VREF: -1V, 1V
Res: will be varied
N=2n will be varied
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
Axis of Symmetry
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Quantization EffectsRes = 4 bits
Some components
very small
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Quantization EffectsRes = 4 bits
Set lower display
limit at -120dB
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
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Quantization EffectsRes = 4 bits
Fundamental
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Quantization EffectsRes = 10 bits
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Res 10 No. points 256 fsig= 50.00 No. Periods 23.00
Rectangular Window
Columns 1 through 5
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Columns 6 through 10
-102.3822 -99.5653 -85.7335 -89.1227 -83.0851
Quantization EffectsRes = 10 bits
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Columns 11 through 15
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Columns 16 through 20
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Columns 21 through 25
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Columns 26 through 30
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Columns 31 through 35
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Columns 36 through 40
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Columns 41 through 45
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Columns 46 through 50
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Columns 51 through 55
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Columns 56 through 60
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Columns 61 through 65
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Columns 66 through 70
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Columns 71 through 75
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Columns 76 through 80
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Columns 81 through 85
-76.6723 -87.0770 -91.5964 -82.1222 -78.7656
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Columns 86 through 90
-82.9621 -93.0224 -116.8549 -93.7327 -75.6231
Columns 91 through 92
-94.4914 -81.0819
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Res 10 No. points 4096 fsig= 50.00 No. Periods 23.00
Rectangular Window
Columns 1 through 5
-55.6060 -97.9951 -107.4593 -103.4508 -120.0000
Columns 6 through 10
-96.7808 -105.2905 -96.7395 -104.5281 -90.7582
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Columns 11 through 15
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Columns 16 through 20
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Columns 21 through 25
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Columns 26 through 30
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Columns 31 through 35
-104.3215 -100.3451 -97.1556 -86.0534 -94.7263
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Columns 36 through 40
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Columns 41 through 45
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Columns 46 through 50
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Columns 51 through 55
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Columns 56 through 60
-91.1056 -101.5798 -94.1031 -95.9163 -83.8407
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Columns 61 through 65
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Columns 66 through 70
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Columns 71 through 75
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Columns 76 through 80
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Columns 81 through 85
-96.5194 -85.8129 -95.1970 -94.8699 -104.9224
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• Distortion Analysis
• Time Quantization Effects
– of DACs
– of ADCs
• Amplitude Quantization Effects
– of DACs
– of ADCs
Spectral Characterization of Data
Converters
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Spectral Characteristics of
DACs and ADCs
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Spectral Characteristics of DAC
t
Periodic Input Signal
Sampling Clock
TSIG t
Sampled Input Signal (showing time points where samples taken)
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Spectral Characteristics of DAC
TSIG
TPERIOD
Quantized Sampled Input Signal (with zero-order sample and hold)
Quantization
Levels
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Spectral Characteristics of DAC
Sampling Clock
TSIG
TPERIOD
TDFT WINDOW
TCLOCK
DFT Clock
TDFT CLOCK
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Spectral Characteristics of DAC
Sampling Clock
TSIG
TPERIOD
TDFT WINDOW
TCLOCK
DFT Clock
TDFT CLOCK
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Spectral Characteristics of DAC
Sampling Clock
DFT Clock
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Spectral Characteristics of DAC
Sampling Clock
DFT Clock
Sampled
Quantized Signal
(zoomed)
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Consider the following example– fSIG=50Hz
– k1=230
– k2=23
– NP=1
– nres=8bits
– Xin(t) =.95sin(2πfSIGt) (-.4455dB)
Thus– NP1=23
– θSR=5
– fCL/fSIG=10
Matlab File: afft_Quantization_DAC.m
Spectral Characteristics of DAC
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nsam = 142.4696
DFT Simulation from Matlab
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nsam = 142.4696
DFT Simulation from MatlabExpanded View
Width of this region is fCL
Analogous to the overall DFT window when directly sampled but modestly asymmetric
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nsam = 142.4696
DFT Simulation from MatlabExpanded View
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nsam = 142.4696
DFT Simulation from MatlabExpanded View
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nsam = 142.4696
DFT Simulation from MatlabExpanded View
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Columns 1 through 7
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Columns 15 through 21
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Columns 29 through 35
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fSIG=50Hz , k1=23, k2=23, NP=1, nres=8bits Xin(t) =sin(2πfSIGt)
N=32768
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Columns 36 through 42
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Columns 64 through 70
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N=32768
fSIG=50Hz , k1=23, k2=23, NP=1, nres=8bits Xin(t) =sin(2πfSIGt)
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Columns 71 through 77
-120.0000 -72.8111 -120.0000 -90.1876 -120.0000 -82.5616 -114.0867
Columns 78 through 84
-89.8269 -115.6476 -80.6553 -120.0000 -86.3818 -120.0000 -88.3454
Columns 85 through 91
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Columns 92 through 98
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Columns 99 through 100
-120.0000 -63.8165
N=32768
fSIG=50Hz , k1=23, k2=23, NP=1, nres=8bits Xin(t) =sin(2πfSIGt)
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nsam = 569.8783
DFT Simulation from Matlab
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nsam = 569.8783
DFT Simulation from MatlabExpanded View
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nsam = 569.8783
DFT Simulation from MatlabExpanded View
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nsam = 569.8783
DFT Simulation from MatlabExpanded View
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Columns 1 through 7
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Columns 8 through 14
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Columns 15 through 21
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Columns 22 through 28
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Columns 29 through 35
-120.0000 -95.8006 -120.0000 -110.7338 -120.0000 -82.3448 -120.0000
fSIG=50Hz , k1=23, k2=23, NP=1, nres=8bits Xin(t) =sin(2πfSIGt)
N=131072
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Columns 36 through 42
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Columns 57 through 63
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Columns 64 through 70
-102.2661 -120.0000 -110.0806 -120.0000 -87.7635 -120.0000 -64.4072
fSIG=50Hz , k1=23, k2=23, NP=1, nres=8bits Xin(t) =sin(2πfSIGt)
N=131072
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Columns 71 through 77
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Columns 92 through 98
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Columns 99 through 100
-120.0000 -108.6808
fSIG=50Hz , k1=23, k2=23, NP=1, nres=8bits Xin(t) =sin(2πfSIGt)
N=131072
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Consider the following example– fSIG=50Hz
– k1=50
– k2=5
– NP=2
– nres=8bits
– Xin(t) = =.95sin(2πfSIGt) (-.4455dB)
Thus– NP1=5
– θSR=5
– NP2=10
Spectral Characteristics of DAC
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nres=8
DFT Simulation from Matlab
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nres=8
DFT Simulation from MatlabExpanded View
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nres=8
DFT Simulation from Matlab
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nres=8
DFT Simulation from MatlabExpanded View
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Columns 1 through 7
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Columns 8 through 14
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Columns 15 through 21
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Columns 22 through 28
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Columns 29 through 35
-78.0140 -120.0000 -47.7412 -120.0000 -85.9233 -120.0000 -27.8207
fSIG=50Hz, k1=50, k2=5, NP=2, nres=8bits, Xin(t) =sin(2πfSIGt)
N=131072
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Columns 36 through 42
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Columns 50 through 56
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Columns 57 through 63
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Columns 64 through 70
-120.0000 -78.2678 -120.0000 -24.9258 -120.0000 -87.6235 -120.0000
fSIG=50Hz, k1=50, k2=5, NP=2, nres=8bits, Xin(t) =sin(2πfSIGt)
N=131072
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Columns 71 through 77
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Columns 78 through 84
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Columns 85 through 91
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Columns 92 through 98
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Columns 99 through 105
-38.8332 -120.0000 -92.1633 -120.0000 -34.7560 -120.0000 -77.1229
fSIG=50Hz, k1=50, k2=5, NP=2, nres=8bits, Xin(t) =sin(2πfSIGt)
N=131072
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nres=8
DFT Simulation from Matlab
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nres=8
DFT Simulation from MatlabExpanded View
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Columns 1 through 7
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Columns 8 through 14
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Columns 15 through 21
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Columns 22 through 28
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Columns 29 through 35
-98.4283 -120.0000 -51.2204 -120.0000 -92.1630 -120.0000 -39.9145
fSIG=50Hz, k1=50, k2=5, NP=2, nres=8bits, Xin(t) =sin(2πfSIGt)
N=1024
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Columns 36 through 42
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Columns 43 through 49
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Columns 50 through 56
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Columns 57 through 63
-89.3186 -120.0000 -45.3675 -120.0000 -62.0430 -120.0000 -46.7029
Columns 64 through 70
-120.0000 -85.3723 -120.0000 -40.6886 -120.0000 -92.0718 -120.0000
N=1024
fSIG=50Hz, k1=50, k2=5, NP=2, nres=8bits, Xin(t) =sin(2πfSIGt)
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Columns 71 through 77
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Columns 78 through 84
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Columns 85 through 91
-85.8692 -120.0000 -41.9049 -120.0000 -89.7301 -120.0000 -19.6301
Columns 92 through 98
-120.0000 -91.5501 -120.0000 -50.5392 -120.0000 -92.8884 -120.0000
Columns 99 through 105
-53.8928 -120.0000 -104.2832 -120.0000 -53.8225 -120.0000 -91.0209
N=1024
fSIG=50Hz, k1=50, k2=5, NP=2, nres=8bits, Xin(t) =sin(2πfSIGt)
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Consider the following example– fSIG=50Hz
– k1=11
– k2=1
– NP=2
– nres=12bits
– Xin(t) = =.95sin(2πfSIGt) (-.4455dB)
Thus– NP1=1
– θSR=11
– NP2=2
Spectral Characteristics of DAC
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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Consider the following example– fSIG=50Hz
– k1=230
– k2=23
– NP=1
– nres=12bits
– Xin(t) = =.95sin(2πfSIGt) (-.4455dB)
Thus– NP1=23
– θSR=10
– NP2=23
Spectral Characteristics of DAC
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
Columns 1 through 7
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Columns 15 through 21
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Columns 29 through 35
-120.0000 -93.8547 -120.0000 -91.4631 -120.0000 -81.9608 -120.0000
fSIG=50Hz k1=230 k2=23 NP=1 nres=12bits Xin(t) = =.95sin(2πfSIGt) (-.4455dB) NP1=23 θSR=10
NP2=23
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DFT Simulation from MatlabColumns 36 through 42
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Columns 43 through 49
-120.0000 -93.3545 -120.0000 -91.6806 -80.7859 -83.9353 -120.0000
Columns 50 through 56
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Columns 64 through 70
-97.5144 -120.0000 -78.9551 -120.0000 -88.4176 -120.0000 -88.0509
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DFT Simulation from Matlab
Columns 71 through 77
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Columns 78 through 84
-97.4143 -120.0000 -81.3430 -120.0000 -87.6762 -120.0000 -96.6112
Columns 85 through 91
-120.0000 -91.5441 -120.0000 -92.0844 -120.0000 -88.0732 -120.0000
Columns 92 through 98
-97.2936 -82.6264 -83.1604 -120.0000 -86.8155 -120.0000 -96.8068
Columns 99 through 100
-120.0000 -90.8133
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Consider the following example– fSIG=50Hz
– k1=230
– k2=23.1
– NP=1
– nres=12bits
– Xin(t) = =.95sin(2πfSIGt) (-.4455dB)
Thus– NP1=23.1
– θSR=9.957
– NP2=23.1
Spectral Characteristics of DAC
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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Consider the following example– fSIG=50Hz
– k1=230
– k2=23
– NP=1
– nres=12bits
– Xin(t) =.88sin(2πfSIGt)+0.1sin(2πfSIGt)
– (-1.11db fundamental, -20dB 2nd harmonic)
Thus– NP1=23
– θSR=10
– NP2=23
Spectral Characteristics of DAC
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DFT Simulation from Matlab
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DFT Simulation from Matlab
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DFT Simulation from Matlab
Columns 1 through 7
-68.2448 -95.4048 -103.0624 -91.5534 -94.3099 -76.5052 -107.8586
Columns 8 through 14
-98.3634 -107.7150 -70.4198 -97.2597 -91.1898 -103.5449 -95.7898
Columns 15 through 21
-108.9130 -94.9846 -102.5323 -91.8645 -90.3773 -80.0818 -107.9922
Columns 22 through 28
-98.3435 -107.5614 -1.2534 -99.6919 -90.7685 -103.9860 -96.1429
Columns 29 through 35
-108.9011 -94.5258 -101.9463 -92.1271 -83.9805 -82.6260 -108.1158
fSIG=50Hz k1=230 k2=23 NP=1 nres=12bits Xin(t) =.88sin(2πfSIGt)+0.1sin(2πfSIGt) (-1.11db
fundamental, -20dB 2nd harmonic) NP1=23 θSR=10 NP2=23
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DFT Simulation from Matlab
Columns 36 through 42
-98.3035 -107.3983 -70.2715 -101.8108 -90.2829 -104.3909 -96.4685
Columns 43 through 49
-108.8814 -94.0244 -101.2937 -92.3447 -20.5694 -84.6007 -108.2298
Columns 50 through 56
-98.2433 -107.2276 -76.1993 -103.7144 -89.7244 -104.7634 -96.7781
Columns 57 through 63
-108.8537 -93.4756 -100.5602 -92.5195 -83.3389 -86.2119 -108.3343
Columns 64 through 70
-98.1627 -107.0564 -79.6196 -105.4341 -89.0818 -105.1065 -82.5417
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DFT Simulation from Matlab
Columns 71 through 77
-108.8180 -92.8737 -99.7264 -92.6536 -89.0679 -87.5695 -108.4295
Columns 78 through 84
-98.0612 -106.9226 -82.0074 -106.9364 -88.3404 -105.4217 -97.1991
Columns 85 through 91
-108.7742 -92.2117 -98.7644 -92.7484 -92.3212 -88.7393 -108.5158
Columns 92 through 98
-97.9383 -82.1713 -83.8248 -108.1091 -87.4797 -105.7091 -97.4305
Columns 99 through 105
-108.7221 -91.4804 -97.6333 -92.8049 -94.5701 -89.7636 -108.5932
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Summary of time and amplitude
quantization assessment
Time and amplitude quantization do not
introduce harmonic distortion
Time and amplitude quantization do
increase the noise floor
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End of Lecture 32